Sonar has long been used for detecting objects on the ocean bottom. A sonar image typically is comprised of a matrix of points or picture elements (pixels) displayed on either a cathode ray tube or paper. The points or pixels have a greyness level ranging from 0 to 255 on a greyness scale. Objects normally appear on sonar images as a highlight shadow combination, particularly when the sonar images are generated by a moving vehicle. A sonar scan for short distances typically comprises a massive amount of data which must be reviewed in short time periods. Prior to the present invention this review was done by human analysts looking at the sonar scans. As the analyst reviews the scans he marks areas where he has inferred specific objects or features may be present on the ocean bottom. Because of the massive amounts of data that must be reviewed in short time periods, human analysts are subject to frequent errors or omissions in both detection and classification.
Automatic detection and classification systems for sonar images do not currently exist. Automatic detection systems developed for optical images do no work well for noisy sonar images. Techniques used on optical images including binarizing the image and using run-length encoding, connectivity analysis of image segments, line and edge detection, and pattern classifiers such as Bayes, least squares, and maximum likelihood do not perform well on sonar images. The noisy nature of sonar images also precludes the use of line and edge detection operators. Extensive filtering of the image to reduce noise, reduces contrast to a level where automatic detection systems which have been tried have generated an exceptionally high false alarm rate. Accordingly, there is a need for an automatic target cuing system for sonar images. The system must have extremely high throughput rates, many hundreds of thousands/sonar picture elements (pixels) per second. Such high rates preclude the use of complicated algorithms on single CPU architectures.